Battery diagnostic device, battery diagnostic method, and battery diagnostic system

Abstract

According to some embodiments disclosed herein, a battery diagnostic device includes a sensor configured to measure time-based first battery data from a battery to be diagnosed, and a controller configured to preprocess the first battery data to generate preprocessed data, generate frequency-based second battery data based on the preprocessed data, and diagnose whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data.

Description

Technical Field

[0001] This application claims the benefit of priority based on Korean Patent Application No. 10-2023-0096958, filed on July 25, 2023, and all the contents disclosed in the literature of the patent application are incorporated herein by reference as part of this specification.

[0002] The embodiments disclosed in this document relate to a battery diagnosis device, a battery diagnosis method, and a battery diagnosis system.

Background Art

[0003] In recent years, research and development on secondary batteries have been actively conducted. Here, a secondary battery is a battery that can be charged and discharged, and can be interpreted to include all conventional Ni / Cd batteries, Ni / MH batteries, etc., and recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have a higher energy density compared to conventional Ni / Cd batteries, Ni / MH batteries, etc., and can be manufactured in a small and light form, so they can have high usability for power sources of mobile devices. In recent years, its scope of use has been extended even to power sources of electric vehicles, and it has attracted attention as a next-generation energy storage medium.

[0004] Diagnosis of whether a battery is abnormal can be performed based on signals measured in the time domain. For example, fluctuations in voltage, current, and temperature over time from a battery can be measured, and based on this, it can be diagnosed whether there are errors or defects in the battery. However, time-based signals can include various noises and measurement errors, and when analyzing long-term patterns, some data may be lost due to the application of filters.

Summary of the Invention

Problems to be Solved by the Invention

[0005] One objective of the embodiments disclosed herein is to provide a battery diagnostic device, a battery diagnostic method, and a battery diagnostic system that can diagnose a battery from a different perspective in order to solve the problem of time-domain-based battery diagnostics.

[0006] The technical problems of the embodiments disclosed in this document are not limited to those mentioned above, and other technical problems not mentioned can be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0007] According to some embodiments disclosed herein, a battery diagnostic device includes a sensor configured to measure time-based first battery data from a battery to be diagnosed, and a controller configured to preprocess the first battery data to generate preprocessed data, generate frequency-based second battery data based on the preprocessed data, and diagnose whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data.

[0008] According to some embodiments, the controller is configured to calculate statistical values ​​for the battery cells of the battery to be diagnosed and the amplitude values ​​of the second battery data corresponding to the frequency values ​​of the second battery data, and to diagnose whether or not there is an abnormality based on the statistical values.

[0009] According to some embodiments, the controller is configured to calculate an error value of the amplitude value based on the statistical value and to diagnose the presence or absence of an abnormality based on the number of error values ​​that exceed an error threshold value.

[0010] According to some embodiments, the controller is configured to calculate the median value of the amplitude for each frequency value, calculate the median value corresponding to the frequency value, and calculate the error value of the amplitude value based on the median value.

[0011] According to some embodiments, the controller is configured to calculate the mean and variance of the error values ​​for each battery cell and to diagnose whether each battery cell is abnormal based on the mean and variance of the error values.

[0012] According to some embodiments, the controller is configured to count the number of frequency values ​​among the frequency values ​​whose error value exceeds an error reference value for each battery cell, and to determine any battery cell in which the number of frequency values ​​exceeding the error reference value exceeds a numerical reference value as an abnormal battery cell.

[0013] According to some embodiments, the controller is configured to determine the abnormal battery cell for each test cycle, count the number of abnormal cells, and when a set number of test cycles have been completed, if the number of abnormal cells exceeds the abnormal cell threshold, the battery to be diagnosed is determined to be an abnormal battery.

[0014] According to some embodiments, the controller is configured to perform time aggregation preprocessing on the first battery data to generate temporally uniform data, and to perform inputting preprocessing on the temporally uniform data to generate the preprocessed data.

[0015] According to some embodiments disclosed herein, a battery diagnostic method includes the steps of: measuring time-based first battery data from a battery to be diagnosed; pre-processing the first battery data to generate pre-processed data; generating frequency-based second battery data based on the pre-processed data; and diagnosing whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data.

[0016] According to some embodiments, the step of diagnosing the presence or absence of an abnormality includes the step of calculating statistical values ​​for the battery cells of the battery to be diagnosed and the amplitude values ​​of the second battery data corresponding to the frequency values ​​of the second battery data, and the step of diagnosing the presence or absence of an abnormality based on the statistical values.

[0017] According to some embodiments, the step of diagnosing the presence or absence of an abnormality includes the step of calculating an error value of the amplitude value based on the statistical value, and the step of diagnosing the presence or absence of an abnormality based on the number of error values ​​that exceed an error reference value among the error values.

[0018] According to some embodiments, the step of calculating the error value includes the step of calculating the median value of the amplitude values ​​for each frequency value of the frequency value to calculate the median value corresponding to the frequency value, and the step of calculating the error value of the amplitude value based on the median value.

[0019] According to some embodiments, the step of diagnosing the presence or absence of abnormalities includes the steps of calculating the mean and variance of the error values ​​for each battery cell, and diagnosing the presence or absence of abnormalities in each battery cell based on the mean and variance of the error values.

[0020] According to some embodiments, the step of diagnosing the presence or absence of an abnormality includes, for each battery cell, the step of counting the number of frequency values ​​whose error value exceeds an error reference value, and determining that any battery cell in which the number of frequency values ​​exceeding the error reference value exceeds a number reference value is an abnormal battery cell.

[0021] According to some embodiments, the step of diagnosing the presence or absence of an abnormality includes the step of determining the abnormal battery cell for each test cycle and counting the number of abnormal cells, and the step of determining the battery to be diagnosed as an abnormal battery if the number of abnormal cells exceeds the abnormal cell threshold when a set number of test cycles have been completed.

[0022] According to some embodiments, the step of generating the preprocessed data includes the step of performing time aggregation preprocessing on the first battery data to generate time-uniform data, and the step of performing missing value imputation preprocessing on the time-uniform data to generate the preprocessed data.

[0023] According to some embodiments disclosed in this document, a battery diagnosis system includes a battery to be diagnosed, a charger configured to apply a test cycle voltage to the battery to be diagnosed, a battery diagnosis device configured to measure time-based first battery data from the battery to be diagnosed, preprocess the first battery data to generate preprocessed data, generate frequency-based second battery data based on the preprocessed data, and diagnose whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data, and a management server configured to manage a diagnosis result for the battery to be diagnosed.

[0024] According to some embodiments, the battery diagnosis device is configured to calculate a statistical value for an amplitude value of the second battery data corresponding to a battery cell of the battery to be diagnosed and a frequency value of the second battery data, and diagnose whether there is an abnormality based on the statistical value.

[0025] According to some embodiments, the battery diagnosis device is configured to calculate an error value of the amplitude value based on the statistical value, and diagnose whether there is an abnormality based on the number of error values exceeding an error reference value among the error values.

[0026] According to some embodiments, the battery diagnosis device is configured to calculate a median value of the amplitude value for each frequency value of the frequency values to calculate a median value corresponding to the frequency value, and calculate an error value of the amplitude value based on the median value.

[0027] According to some embodiments, the battery diagnosis device is configured to calculate an average and a variance of the error values for each battery cell, and diagnose whether each battery cell is abnormal based on the average and the variance of the error values.

[0028] According to some embodiments, the battery diagnosis device counts, for each battery cell, the number of frequency values among the frequency values for which the error value exceeds the error reference value, and determines a battery cell for which the number of frequency values exceeding the error reference value exceeds a number reference value as an abnormal battery cell.

[0029] According to some embodiments, the battery diagnostic device determines the abnormal battery cells for each test cycle, counts the number of abnormal cells, and when the set number of test cycles is completed, if the number of abnormal cells exceeds the abnormal cell reference value, the diagnostic target battery is determined to be an abnormal battery.

[0030] According to some embodiments, the battery diagnostic device performs time-aggregation preprocessing on the first battery data to generate temporally uniform data, and performs missing value complementation preprocessing on the temporally uniform data to generate the preprocessed data.

Advantages of the Invention

[0031] According to the embodiments disclosed in this document, in order to solve the problems of battery diagnosis based on the time domain, a battery diagnostic device, a battery diagnostic method, and a battery diagnostic system that can diagnose a battery from other viewpoints can be provided.

[0032] The technical effects of the embodiments disclosed in this document are not limited to the effects mentioned above, and other effects not mentioned are clearly understandable to those skilled in the art from the disclosure of this document.

Brief Description of the Drawings

[0033] [Figure 1] Shows the components constituting a battery diagnostic system according to some embodiments. [Figure 2] Shows the components constituting a battery diagnostic device and the operation of the battery diagnostic device according to some embodiments. [Figure 3] Shows the detailed operation process of a battery diagnostic device according to some embodiments. [Figure 4] Shows the process of performing preprocessing on the first battery data according to some embodiments to generate preprocessed data. [Figure 5] Shows the process of performing region conversion on the preprocessed data according to some embodiments to generate frequency-based second battery data. [Figure 6] The amplitude values ​​(A11, ..., Anm) for battery cells (c1, ..., cn) and frequency values ​​(f1, ..., fm) according to some embodiments are shown. [Figure 7] The process for calculating the median value (Mj) of the amplitude values ​​(A1j, ..., Anj) for each frequency value (fj) according to some embodiments is shown. [Figure 8] The process for calculating the error values ​​(E1j, ..., Enj) of the amplitude values ​​(A1j, ..., Anj) based on the median value (Mj) according to some embodiments is shown. [Figure 9] The process for calculating the error reference value (xi1 + 4*xi2) for each battery cell (ci) according to some embodiments is shown. [Figure 10] This describes the process of selecting battery cells (c1, ..., cn) according to some embodiments in which the number of frequency values ​​exceeding the error reference value exceeds the number reference value. [Figure 11] This describes the process of identifying an abnormal battery cell for each test cycle according to one embodiment and counting the number of abnormal cells. [Figure 12] This describes the process of determining whether the battery under diagnosis is abnormal based on the number of abnormal cells when a predetermined number of test cycles have been completed according to some embodiments. [Figure 13] The steps comprising a battery diagnostic method according to one embodiment are shown below. [Modes for carrying out the invention]

[0034] The embodiments described herein are described below with reference to the accompanying drawings. However, this is not intended to limit the disclosures herein to any particular embodiment, and should be understood to include various modifications, equivalents, and / or alternatives to the embodiments described herein.

[0035] The embodiments and terminology used herein are not intended to limit the technical features described herein to any particular embodiment, but should be understood to include various modifications, equivalents, or substitutes of such embodiments. In relation to the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more such items unless the context clearly indicates otherwise.

[0036] In this document, each phrase such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B, or C,” “at least one of A, B, and C,” and “at least one of A, B, or C” may include any one of the items listed together with the applicable phrase, or any possible combination thereof. Terms such as “first,” “second,” “first,” “second,” “A,” “B,” “(a),” or “(b)” may be used merely to distinguish one component from other components and, unless otherwise stated, do not limit the component in any other respect (e.g., importance or order).

[0037] Wherever a component (e.g., the first) is referred to as being "coupled," "joined," or "connected" to another component (e.g., the second), with or without such terms, it means that the first component may be directly (e.g., wired or wirelessly) or indirectly (e.g., via the third component) connected to the other component.

[0038] Methods according to various embodiments disclosed herein may be provided in a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of an instrument-readable storage medium (e.g., compact disc read-only memory, CD-ROM) or online (e.g., download or upload) via an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily generated in an instrument-readable storage medium such as the memory of a manufacturer's server, an application store server, or an intermediary server.

[0039] According to the embodiments disclosed herein, each of the aforementioned components (e.g., a module or a program) may include one or more individuals, and some of the individuals may be separated and arranged in other components. According to the embodiments disclosed herein, one or more of the aforementioned components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the components of the multiple components before the integration. According to the embodiments disclosed herein, operations performed by a module, program, or other component may be performed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be performed in a different order, omitted, or one or more other operations may be added.

[0040] Figure 1 shows the elements that constitute a battery diagnostic system according to one embodiment. Referring to Figure 1, the battery diagnostic system 100 may include a charger / discharger 110, a battery to be diagnosed 120, a battery diagnostic device 130, and a management server 140. However, it is not limited to this, and some components may be omitted from the battery diagnostic system 100, and other general-purpose components may be further included in the battery diagnostic system 100.

[0041] The battery diagnostic system 100 may refer to a system for diagnosing the state of the battery 120 to be diagnosed. According to the embodiment, a test voltage can be applied to the battery 120 to be diagnosed by the charger / discharger 110, and in response to the test voltage, a time-domain signal output by the battery 120 to be diagnosed can be measured by the battery diagnostic device 130. The time-domain signal can be converted into frequency-domain data, and based on the frequency-domain data, it is possible to determine whether or not there is an abnormality in the battery 120 to be diagnosed.

[0042] According to one embodiment, the charger / discharger 110 can be configured to apply a test cycle voltage to the battery 120 under diagnosis. The test cycle voltage may have a profile that charges and then discharges the battery 120 under diagnosis and can be applied to the battery 120 multiple times. The number of times the test cycle voltage is applied can be set to 50, 100, 200, or any other suitable number. For example, the charger / discharger 110 may include a power supply device for applying various forms of voltage and current to the battery 120 under diagnosis.

[0043] According to the embodiment, the battery 120 to be diagnosed can be the subject of diagnosis by the battery diagnostic system 100. The battery 120 to be diagnosed may include multiple battery cells. For example, the battery 120 to be diagnosed may include multiple battery modules, and each module of the multiple battery modules may include multiple battery cells. According to the embodiment, in each cycle of multiple test cycles, if the number of battery cells with errors is greater than or equal to a certain number, the cycle can be counted as a cycle with errors, and when multiple test cycles are completed, if the count of cycles with errors is greater than or equal to a certain value, it can be determined that there is an abnormality in the battery 120 to be diagnosed.

[0044] The battery diagnostic device 130 can perform a procedure to determine whether or not there is an abnormality in the battery 120 to be diagnosed. According to the embodiment, the battery diagnostic device 130 can convert time-domain data into frequency-domain data and perform additional processing on the frequency-domain data to determine whether or not there is an abnormality.

[0045] According to one embodiment, the management server 140 can be configured to manage diagnostic results for the battery 120 to be diagnosed. The management server 140 can be connected to the battery diagnostic device 130 via wired / wireless data communication, and can receive and record data such as the status of the battery 120 to be diagnosed, whether there are any abnormalities, and the diagnostic results from the battery diagnostic device 130. The management server 140 can control the battery diagnostic device 130 or manage the battery 120 to be diagnosed at the request of a system administrator or battery user.

[0046] According to the embodiment, the management server 140 can perform at least a portion of the procedures performed by the battery diagnostic device 130 on behalf of the battery diagnostic device 130. The management server 140 can receive data necessary for diagnosing the battery 120 to be diagnosed from the battery diagnostic device 130, perform the diagnostic procedure, and transmit the results to the battery diagnostic device 130. According to the embodiment, the management server 140 can install energy management software necessary for diagnosing the battery 120 to be diagnosed on the battery diagnostic device 130, and can provide the battery diagnostic device 130 with update information for the energy management software.

[0047] Figure 2 shows the elements constituting a battery diagnostic device according to some embodiments, as well as the operation of the battery diagnostic device. Referring to Figure 2, the battery diagnostic device 130 may include a sensor 131 and a controller 132. However, it is not limited to these, and some components may be omitted from the battery diagnostic device 130, and other general-purpose components may be further included in the battery diagnostic device 130.

[0048] According to the embodiment, the sensor 131 and controller 132 in the battery diagnostic device 130 can be electrically connected to each other via inter-device communication methods such as a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), and MIPI (mobile industry processor interface).

[0049] The sensor 131 of the battery diagnostic device 130 can be configured to measure time-based first battery data from the battery 120 to be diagnosed. When a test cycle voltage is applied to the battery 120 to be diagnosed by the charger / discharger 110, an output voltage in response can be generated in the battery 120, and the sensor 131 can measure this to generate first battery data. For this purpose, the sensor 131 may include measuring means such as a voltmeter, ammeter, and thermometer. According to the embodiment, the first battery data may include time-domain signals. For example, the first battery data may include time-domain voltage data, current data, and / or temperature data.

[0050] The controller 132 may have a structure for executing instructions that enable the operation of the battery diagnostic device 130. The controller 132 can be implemented as an array of multiple logic gates for processing various operations or as a general-purpose microprocessor, and can consist of a single processor or multiple processors. For example, the controller 132 can be implemented in the form of at least one of the following: a microprocessor, a CPU, a GPU, and an AP.

[0051] The controller 132 can be configured separately from or integrated with a memory (not shown) configured to store instruction words, and can execute the instruction words stored in the memory to process various operations. The memory can store various data, instruction words, mobile applications, computer programs, etc. For example, the memory can be implemented as a non-volatile memory such as ROM, PROM, EPROM, EEPROM, flash memory, PRAM, MRAM, FRAM®, or a volatile memory such as DRAM, SRAM, SDRAM, RRAM®, and can be implemented in the form of an HDD, SSD, SD, Micro-SD, or a combination thereof.

[0052] The controller 132 of the battery diagnostic device 130 can be configured to preprocess the first battery data to generate preprocessed data. The preprocessing performed on the first battery data may include a preparatory process for converting the first battery data in the time domain to second battery data in the frequency domain. For example, the preprocessing may include a process for equalizing the data over time and / or filling in missing values.

[0053] The controller 132 of the battery diagnostic device 130 can be configured to generate frequency-based second battery data based on preprocessing data. Domain transformation performed on the preprocessing data to generate the second battery data may mean transforming time-domain signals / data into frequency-domain signals / data. According to the embodiment, the domain transformation may include a Fast Fourier Transform (FFT). The second battery data may include amplitude values ​​that vary with frequency values, and these frequency values ​​may consist of continuous or discrete values.

[0054] The controller 132 of the battery diagnostic device 130 can be configured to diagnose whether or not there is an abnormality in the battery 120 to be diagnosed based on statistical data related to the second battery data. The second battery data can indicate the amplitude values ​​of the battery cells of the battery 120 to be diagnosed within a given frequency range, and the presence or absence of an abnormality in the battery 120 to be diagnosed can be determined using statistical variables based on these values. According to the embodiment, the statistical data related to the second battery data may include the mean, median, first quartile, third quartile, variance, standard deviation, etc., for the values ​​of the second battery data.

[0055] Figure 3 shows a detailed operation process of a battery diagnostic device according to one embodiment. Referring to Figure 3, the detailed operation process of the battery diagnostic device 130 may include a first step 310 for data measurement, a second step 320 for time aggregation, a third step 330 for missing value completion, a fourth step 340 for region conversion, a fifth step 350 for statistical value calculation, and a sixth step 360 for diagnosis of abnormality.

[0056] Unlike conventional diagnostic methods that use time-based signals, steps 1 310 to 6 360 can diagnose the presence or absence of abnormalities in the battery 120 to be diagnosed using frequency-based data. Conventional time-based methods utilize a method that detects instantaneous differences in signals, but the frequency-based method of steps 1 310 to 6 360 can detect frequency components of signals that are hidden by noise, and based on this, it is possible to detect cells that have patterns outside the normal range. For example, considering that the characteristics of battery cells are similar, an abnormal cell may show a frequency pattern different from the pattern of other cells. Therefore, by defining the normal range based on statistical values, it is possible to detect the occurrence of continuous abnormal patterns even after noise has been removed.

[0057] Figure 4 shows the process of generating preprocessed data by performing preprocessing on the first battery data according to some embodiments. Referring to Figure 4, we see the first graph 410 showing the waveform of the first battery data measured in the time domain, the second graph 420 showing the waveform of the temporally uniform data generated as a result of time aggregation preprocessing on the first battery data, and the third graph 430 showing the waveform of the preprocessed data generated as a result of missing value imputation preprocessing on the temporally uniform data.

[0058] The first graph 410, the second graph 420, and the third graph 430 can all show voltage on the vertical axis against time on the horizontal axis, so that the first battery data, the time-uniform data, and the pre-processed data may all be time-domain data. The first battery data, the time-uniform data, and the pre-processed data may all have periodicity, where one period may correspond to one test cycle.

[0059] According to the embodiment, the controller 132 of the battery diagnostic device 130 can be configured to perform time aggregation preprocessing on the first battery data to generate time-uniform data, and to perform missing value imputation preprocessing on the time-uniform data to generate preprocessed data. Time aggregation preprocessing allows the values ​​of the first battery data to be uniformly adjusted with respect to time, thereby generating time-uniform data. Missing value imputation preprocessing allows missing values ​​to be imputed by polarization if missing values ​​exist in the time-uniform data based on a given time step value.

[0060] Figure 5 shows the process of generating frequency-based second battery data by performing domain transformation on preprocessed data according to some embodiments. Referring to Figure 5, a third graph 430 is shown, which displays the waveform of the preprocessed data, and a fourth graph 510 is shown, which displays the second battery data in the frequency domain generated as a result of domain transformation on the preprocessed data. The fourth graph 510 can be shown as the amplitude value on the vertical axis with respect to frequency on the horizontal axis.

[0061] When a domain transformation is performed on the time-based preprocessed data shown in the third graph 430, the frequency-based second battery data shown in the fourth graph 510 can be generated. According to the embodiment, the domain transformation performed on the preprocessed data to generate the second battery data may include a Fast Fourier Transform (FFT) or various other types of time-frequency transformations.

[0062] Figure 6 shows the amplitude values ​​(A11, ..., Anm) for battery cells (c1, ..., cn) and frequency values ​​(f1, ..., fm) according to some embodiments.

[0063] Referring to Figure 6, the first to third graphs 610, 620, and 630 are shown, which represent the frequency-based second battery data generated for the first battery cell (c1), second battery cell (c2), and third battery cell (c3) of the battery 120 being diagnosed, respectively.

[0064] The second battery data shown in the first to third graphs 610, 620, and 630 can be reconstructed as shown in the amplitude diagram 640 for the battery cells (c1, ..., cn) of the battery under diagnosis 120 and the frequency values ​​(f1, ..., fm) of the second battery data. If the battery under diagnosis 120 contains n battery cells (c1, ..., cn) and the second battery data contains m frequency values ​​(f1, ..., fm), then each battery cell (ci) can have amplitude values ​​(Ai1, ..., Aim) for the m frequency values ​​(f1, ..., fm).

[0065] According to the embodiment, the controller 132 of the battery diagnostic device 130 can be configured to calculate statistical values ​​for the second battery data amplitude values ​​(A11, ..., Anm) corresponding to the battery cells (c1, ..., cn) and frequency values ​​(f1, ..., fm) of the battery 120 to be diagnosed, and to diagnose the presence or absence of abnormalities based on the statistical values. The statistical values ​​for the amplitude values ​​(A11, ..., Anm) can be derived in various ways. According to the embodiment, in the amplitude diagram 640, statistical values ​​can be calculated for the same frequency value and all battery cells in the vertical direction, or for the same battery cell and all frequency values ​​in the horizontal direction.

[0066] Figure 7 shows the process of calculating the median value (Mj) of the amplitude values ​​(A1j, ..., Anj) for each frequency value (fj) according to some embodiments. Referring to Figure 7, the amplitude diagram shows examples where statistical values ​​are calculated for the same frequency value and all battery cells in the vertical direction, including the calculation of the first statistical value for the first frequency (f1) and all battery cells (c1, ..., cn) 710, and the calculation of the second statistical value for the second frequency (f2) and all battery cells (c1, ..., cn) 720.

[0067] In the case of the first statistical value calculation 710, statistical values ​​can be calculated for the amplitude (A11) of the first battery cell (c1) at the first frequency (f1), the amplitude (A21) of the second battery cell (c2) at the first frequency (f1), the amplitude (A31) of the third battery cell (c3) at the first frequency (f1), and so on. In this case, the statistical value may be the median. According to the embodiment, the statistical value may further include the mean, the first quartile, the third quartile, the variance, the standard deviation, and so on. Similar to the case of the first frequency (f1), statistical values ​​can be calculated for all frequency values ​​(f1, ..., fm).

[0068] According to this embodiment, the controller 132 of the battery diagnostic device 130 can be configured to calculate the median (Mj) of the amplitude values ​​(A1j, ..., Anj) for each frequency value (fj) to calculate the median (M1, ..., Mm) corresponding to the frequency values ​​(fj, ..., fm), and to calculate the error values ​​(E1j, ..., Enj) of the amplitude values ​​(A1j, ..., Anj) based on the median (Mj). According to this embodiment, other types of representative values ​​such as the mean, first quartile, third quartile, variance, and standard deviation can be used instead of the median (Mj).

[0069] Figure 8 shows the process of calculating the error values ​​(E1j, ..., Enj) of the amplitude values ​​(A1j, ..., Anj) based on the median value (Mj) according to some embodiments.

[0070] Referring to Figure 8, the process of calculating the error value of the amplitude value that the battery cell has for a given frequency value in the amplitude diagram is shown in relation to the error calculation 810 of the first statistical value calculation 710.

[0071] In error calculation 810, the first median (M1) calculated for the first frequency (f1) is used to calculate the error of the amplitude value (A11), and can be similarly used to calculate the errors of the remaining amplitude values ​​(A21, A31, ...). On the other hand, in addition to the median, various types of representative values ​​can be used for error calculation. According to the embodiment, the mean, first quartile, third quartile, variance, standard deviation, etc., can be used as representative values ​​for error calculation. According to the embodiment, the difference between the median and the variance can be expressed as the error value for each frequency.

[0072] According to one embodiment, the controller 132 of the battery diagnostic device 130 can be configured to calculate an error value of the amplitude value based on statistical values ​​and to diagnose whether there is an abnormality based on the number of error values ​​that exceed an error reference value. Once all error values ​​have been calculated, as in the error calculation 810, it is possible to analyze how many frequencies in each battery cell exceed the error reference value. For example, for the first battery cell (c1), the number of frequency values ​​whose error value exceeds the error reference value out of 100 frequency values ​​(f1 to f100) can be counted, and if the count value exceeds a number reference value (e.g., 10), the first battery cell (c1) can be classified as an abnormal cell.

[0073] Figure 9 shows the process of calculating the error reference value (xi1 + 4*xi2) for each battery cell (ci) according to some embodiments. Referring to Figure 9, the mean (x11) and variance (x12) of the error values ​​(E11, E12, E13, ...) can be calculated for the first battery cell 910, and an error reference value can be calculated for the first battery cell 910 based on the mean (x11) and variance (x12). Similarly, error reference values ​​can be calculated for the second battery cell 920 and the remaining battery cells.

[0074] According to the embodiment, the controller 132 of the battery diagnostic device 130 can be configured to calculate the mean (xi1) and variance (xi2) of the error values ​​(Ei1, ..., Eim) for each battery cell (ci), and to diagnose whether each battery cell (ci) is abnormal based on the mean (xi1) and variance (xi2) of the error values ​​(Ei1, ..., Eim). According to the embodiment, in addition to the mean and variance, other values ​​such as the median, first quartile, third quartile, and standard deviation can be used to calculate the error reference value.

[0075] According to the embodiment, the controller 132 of the battery diagnostic device 130 can be configured to count the number of frequency values ​​(fj) among the frequency values ​​(f1, ..., fm) in each battery cell (ci) in which the error value (Eij) exceeds the error reference value (xi1 + 4 * xi2), and to determine battery cells (c1, ..., cn) in which the number of frequency values ​​exceeding the error reference value exceeds a number reference value as abnormal battery cells. According to the embodiment, the coefficients 1 and 4 of the error reference value (xi1 + 4 * xi2) can be changed to other suitable values ​​as needed.

[0076] The number of faulty cells threshold value may be a value preset according to the diagnostic performance requirements of the battery diagnostic device 130, and the lower the number of faulty cells threshold value, the higher the diagnostic performance may be. For example, if the number of faulty cells threshold value is set to 10, and in the first battery cell (c1), 12 out of 100 frequency values ​​(f1 to f100) exceed the error threshold value, the first battery cell (c1) can be classified as a faulty cell.

[0077] Figure 10 shows the process of selecting battery cells (c1, ..., cn) according to some embodiments in which the number of frequency values ​​exceeding the error reference value exceeds the number reference value.

[0078] Referring to Figure 10, if the number of frequencies with errors exceeding the error threshold (e.g., 2) in the first battery cell 1010 exceeds the number threshold (e.g., 1), the first battery cell 1010 can be classified as a defective cell.

[0079] Similarly, in the second battery cell 1020, if the number of frequencies with errors exceeding the error threshold (e.g., 1) does not exceed the number threshold (e.g., 1), the second battery cell 1020 can be classified as a normal cell, and the remaining battery cells can be classified as either defective or normal. Therefore, once one test cycle is completed, the number of battery cells classified as defective among the battery cells of the battery 120 being diagnosed can be derived.

[0080] Figure 11 shows the process of identifying an abnormal battery cell for each test cycle according to one embodiment and counting the number of abnormal cells. Referring to Figure 11, the process of counting the number of abnormal battery cells for each test cycle, as described above, can be repeated for multiple test cycles.

[0081] For example, in test cycle 1, the number of battery cells classified as defective may be 3, in test cycle 2, the number of battery cells classified as defective may be 2, and in the remaining test cycles, the number of battery cells classified as defective may be 0. In this case, the cumulative count value of 5 can be compared to a reference value to determine the final defect of the battery 120 under diagnosis. According to the embodiment, the total number of test cycles may be 100, and the comparison reference value for the cumulative count value may be 3. However, different suitable values ​​can be used depending on changes in the diagnostic performance requirements. According to the embodiment, when high diagnostic performance is required, the total number of test cycles may be higher, and the comparison reference value may be lower.

[0082] According to one embodiment, the controller 132 of the battery diagnostic device 130 can be configured to determine an abnormal battery cell for each test cycle, count the number of abnormal cells, and, when a set number of test cycles have been completed, determine the battery 120 to be diagnosed as an abnormal battery if the number of abnormal cells exceeds the abnormal cell threshold. For example, when 100 test cycles have been completed, if the count value of the number of abnormal cells exceeds 3, it can be determined that the battery 120 to be diagnosed is ultimately abnormal.

[0083] Figure 12 shows the process of determining whether the battery under diagnosis is abnormal based on the number of abnormal cells when a pre-set number of test cycles have been completed according to some embodiments.

[0084] Referring to Figure 12, a flowchart 1200 is shown to explain the fluctuation in the count value indicating the number of battery cells classified as defective during a set number of test cycles.

[0085] In the first step 1210, the count value can be confirmed, and in the second step 1220, the count value can be compared with a reference value. In the third step 1230, if the count value exceeds the reference value, a notification can be made that there is an abnormality in the battery 120 to be diagnosed. If the count value does not exceed the reference value, in the fourth step 1240, it can be confirmed whether the already set number of test cycles have been completed. If the already set number of cycles have been completed, in the fifth step 1250, the count value can be initialized to 0.

[0086] If the previously set number of cycles has not been completed, in step 6, 1260, it is determined whether no abnormal cells were detected and the count value did not increase during a specific number of consecutive test cycles (e.g., 5). If "Yes," in step 7, 1270, the total cumulative count value can be decreased by 1. If "No," in step 8, 1280, the total cumulative count value can be maintained.

[0087] In relation to steps 6 (1260) to 8 (1280), if no abnormal cells are detected during a specific number of consecutive test cycles (e.g., 5), this may mean that the battery 120 under diagnosis has stabilized, or that abnormal cells were detected due to temporary factors. Therefore, such a calibration process can improve the accuracy of the battery diagnostic device 130.

[0088] Figure 13 shows the steps that constitute a battery diagnostic method according to one embodiment. Referring to Figure 13, the battery diagnostic method 1300 may include steps 1310 to 1340. However, it is not limited to this, and some steps may be omitted or other general steps may be added, and the steps of the battery diagnostic method 1300 may be performed in an order different from that shown.

[0089] The battery diagnostic method 1300 can consist of steps processed chronologically in the battery diagnostic device 130. Therefore, even if some details are omitted below, the information described above regarding the battery diagnostic device 130 can be similarly applied to the battery diagnostic method 1300.

[0090] Steps 1310 to 1340 of the battery diagnostic method 1300 can be performed by the sensor 131 and controller 132 of the battery diagnostic device 130.

[0091] In step 1310, the battery diagnostic device 130 can measure time-based first battery data from the battery to be diagnosed via a sensor. In step 1320, the battery diagnostic device 130 can preprocess the first battery data via the controller to generate preprocessed data.

[0092] In step 1330, the battery diagnostic device 130 can generate frequency-based second battery data based on the preprocessing data via the controller.

[0093] In step 1340, the battery diagnostic device 130 can diagnose whether or not there is an abnormality in the battery to be diagnosed based on statistical data related to the second battery data, via the controller.

[0094] On the other hand, the battery diagnostic method 1300 can be implemented in the form of a computer program stored on a computer-readable storage medium. That is, the computer program may include instructions for implementing the battery diagnostic method 1300, and the instructions of the program may be stored on a computer-readable storage medium. The computer program may include a mobile application.

[0095] For example, computer-readable storage media can include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute computer program instructions, such as ROM, RAM, and flash memory. Computer program instructions can include machine code created by a compiler and high-level language code that can be executed by a computer using an interpreter or the like.

[0096] The terms “contain,” “constitute,” or “have,” as used above, mean “may contain,” and should not be interpreted as meaning that they may contain, unless otherwise specified, other components, rather than excluding them. All terms, including technical or scientific terms, should have the same meaning as that generally understood by a person of ordinary skill in the art to which the embodiments disclosed herein belong, unless otherwise specified. Commonly used terms, such as those defined in dictionaries, should be interpreted to be consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined herein.

[0097] The above description is merely illustrative of the technical concept disclosed herein, and any person with ordinary skill in the art to which the embodiments disclosed herein belong can make various modifications and variations without departing from the essential characteristics of the embodiments disclosed herein. Therefore, the embodiments disclosed herein are for illustrative purposes only, not to limit the technical concept of the embodiments disclosed herein, and the scope of the technical concept disclosed herein is not limited by such embodiments. The scope of protection of the technical concept disclosed herein must be interpreted according to the claims described below, and all technical concepts within an equivalent scope should be interpreted as being included in the scope of rights of this document. [Explanation of Symbols]

[0098] 100: Battery diagnostic system 110: Charger / discharger 120: Battery to be diagnosed 130: Battery diagnostic device 131: Sensor 132: Controller 140: Management Server

Claims

1. A sensor configured to measure time-based primary battery data from the battery to be diagnosed, The first battery data is preprocessed to generate preprocessed data. Based on the aforementioned preprocessed data, frequency-based second battery data is generated. A controller configured to diagnose whether or not there is an abnormality in the battery to be diagnosed based on statistical data related to the second battery data, Battery diagnostic device, including

2. The controller calculates statistical values ​​for the battery cells of the battery to be diagnosed and the amplitude values ​​of the second battery data corresponding to the frequency values ​​of the second battery data, The battery diagnostic device according to claim 1, configured to diagnose the presence or absence of the abnormality based on the aforementioned statistical values.

3. The controller calculates the error value of the amplitude value based on the statistical value, The battery diagnostic device according to claim 2, configured to diagnose the presence or absence of the abnormality based on the number of error values ​​that exceed an error reference value among the aforementioned error values.

4. The controller calculates the median value of the amplitude for each frequency value and calculates the median value corresponding to the frequency value. The battery diagnostic device according to claim 3, configured to calculate the error value of the amplitude value based on the median value.

5. The controller calculates the mean and variance of the error values ​​for each battery cell, The battery diagnostic device according to claim 4, configured to diagnose whether each battery cell is abnormal based on the mean and variance of the aforementioned error values.

6. The controller counts the number of frequency values ​​among the frequency values ​​whose error value exceeds the error reference value for each battery cell. The battery diagnostic device according to claim 5, configured to determine battery cells in which the number of frequency values ​​exceeding the error reference value exceeds the number reference value as abnormal battery cells.

7. The controller determines the abnormal battery cell for each test cycle and counts the number of abnormal cells. The battery diagnostic device according to claim 6, configured to determine the battery to be diagnosed as an abnormal battery when the number of abnormal cells exceeds the abnormal cell threshold value after a set number of test cycles have been completed.

8. The controller performs time-based preprocessing on the first battery data to generate time-uniform data. A battery diagnostic device according to any one of claims 1 to 7, configured to generate the preprocessed data by performing preprocessing to impute missing values ​​on the aforementioned time-uniform data.

9. A step of measuring time-based first battery data from the battery to be diagnosed, The steps include: preprocessing the first battery data to generate preprocessed data; The steps include generating frequency-based second battery data based on the aforementioned preprocessing data, A step of diagnosing whether the battery to be diagnosed is abnormal based on statistical data related to the second battery data, Battery diagnostic methods, including those mentioned above.

10. The step of diagnosing the presence or absence of the aforementioned abnormality is: A step of calculating statistical values ​​for the battery cell of the battery to be diagnosed and the amplitude value of the second battery data corresponding to the frequency value of the second battery data, The battery diagnostic method according to claim 9, comprising the step of diagnosing the presence or absence of the abnormality based on the statistical values.

11. The step of diagnosing the presence or absence of the aforementioned abnormality is: A step of calculating the error value of the amplitude value based on the aforementioned statistical value, The battery diagnostic method according to claim 10, comprising the step of diagnosing the presence or absence of the abnormality based on the number of error values ​​that exceed an error reference value among the aforementioned error values.

12. The step of calculating the aforementioned error value is: A step of calculating the median value of the amplitude for each of the aforementioned frequency values ​​and calculating the median value corresponding to the aforementioned frequency value, The battery diagnostic method according to claim 11, comprising the step of calculating an error value of the amplitude value based on the median value.

13. The step of diagnosing the presence or absence of the aforementioned abnormality is: A step of calculating the mean and variance of the error value for each battery cell, The battery diagnostic method according to claim 12, comprising the step of diagnosing whether each battery cell is abnormal based on the mean and variance of the error values.

14. The step of diagnosing the presence or absence of the aforementioned abnormality is: For each battery cell, the step of counting the number of frequency values ​​among the frequency values ​​whose error value exceeds the error reference value, The battery diagnostic method according to claim 13, comprising the step of determining among the battery cells that any battery cells in which the number of frequency values ​​exceeding the error reference value exceeds the number reference value are abnormal battery cells.

15. The step of diagnosing the presence or absence of the aforementioned abnormality is: The steps include: determining the abnormal battery cell for each test cycle and counting the number of abnormal cells; The battery diagnostic method according to claim 14, further comprising the step of determining the battery to be diagnosed as an abnormal battery if, when a set number of test cycles has been completed, the number of abnormal cells exceeds the abnormal cell threshold value.

16. The step of generating the aforementioned preprocessing data is: The steps include: performing time-based preprocessing on the first battery data to generate time-uniform data; A battery diagnostic method according to any one of claims 9 to 15, comprising the step of performing a preprocessing step to impute missing values ​​on the temporally uniform data to generate the preprocessed data.

17. The battery to be diagnosed, A charger / discharger configured to apply a test cycle voltage to the battery to be diagnosed, A battery diagnostic device configured to measure time-based first battery data from the battery to be diagnosed, preprocess the first battery data to generate preprocessed data, generate frequency-based second battery data based on the preprocessed data, and diagnose whether or not there is an abnormality in the battery to be diagnosed based on statistical data related to the second battery data, A management server configured to manage diagnostic results for the aforementioned battery to be diagnosed, A battery diagnostic system, including a battery diagnostic system.

18. The battery diagnostic device calculates statistical values ​​for the battery cells of the battery to be diagnosed and the amplitude values ​​of the second battery data corresponding to the frequency values ​​of the second battery data, The battery diagnostic system according to claim 17, configured to diagnose the presence or absence of the abnormality based on the aforementioned statistical values.

19. The battery diagnostic device calculates the error value of the amplitude value based on the statistical value, The battery diagnostic system according to claim 18, configured to diagnose the presence or absence of the abnormality based on the number of error values ​​that exceed an error reference value among the aforementioned error values.

20. The battery diagnostic device calculates the median value of the amplitude value for each of the frequency values ​​and calculates the median value corresponding to the frequency value. The battery diagnostic system according to claim 19, configured to calculate an error value of the amplitude value based on the median value.

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